Breast Asymmetry, Distortion and Density Are Key Factors for False Positive Decisions
Aim: Understanding both normal mammographic appearance and how false positive (FP) errors occur is paramount to improving the efficiency and diagnostic accuracy of screening mammography services. While much of the focus of research is on increasing knowledge about the appearances and imaging of breast cancers, this study reports on findings where breast screen readers are asked to comment on past incorrect decisions by assigning a lexicon that best describes a known FP region. Method: Fifteen breast screen readers were given two tasks. The first was to assess nine normal screening cases which had attracted a high number of FP decisions in a test set of 60 cases in a previous study with 129 readers. In the second task, the 15 readers in this study, who were made aware that the nine cases were normal, were directed to view distinct regions of interest (ROI) that represented the FP markings from past readings in the blinded observer performance study. A list of descriptors derived from literature was used to assist readers to describe the mammographic appearance within those ROIs. Results: In the first task, readers identified breast density as the greatest difficulty in determining normality. In the second task, asymmetry of breast tissue and a suspicion of architectural distortion (AD) were the top two reasons our readers gave to explain the high number of past FP decisions. Additionally, our readers believed past FP decisions were less likely to reflect a suspicion of breast lesions or masses (second task). Conclusion: The classification of normal cases remains a challenging task, influenced by asymmetry and breast density. FP decisions may reflect a suspicion of AD and appear less related to suspicion of masses.
KeywordsMammography Normal mammograms False positive Breast density Breast tissue asymmetry
This study was supported by the National Breast Cancer Foundation (Australia), the Breastscreen Reader Assessment Strategy (BREAST), RANZCR, The University of Sydney and National Healthcare Group Diagnostic (Singapore).
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